University of Technology Sydney

C04418v1 Master of Data Science in Quantitative Finance

Award(s): Master of Data Science in Quantitative Finance (MDataScQF)
CRICOS code: 104625B
Commonwealth supported place?: No
Load credit points: 96
Course EFTSL: 2
Location: City campus

Overview
Career options
Course intended learning outcomes
Admission requirements
Recognition of prior learning
Course duration and attendance
Course structure
Course completion requirements
Course program
Articulation with UTS courses
Other information

Overview

The Master of Data Science in Quantitative Finance provides students with cutting-edge skills, knowledge and tools allowing them to tackle data problems on a new scale and complexity for tasks in portfolio optimisation, market modelling and credit risk. Data science techniques including machine learning have already transformed the world of asset management, changing how sophisticated investors approach portfolio construction and risk management.

This course combines an internationally recognised quantitative finance program with skills and knowledge in financial data science and statistical modelling.

Career options

Career opportunities for graduates include quantitative analyst, data scientist, data analyst, quantitative analyst, quantitative structurer, quantitative developer, forecaster, trader, model verification, financial engineer, market risk analyst, credit risk analyst, data engineer, data modeller, and investment analyst and financial engineer across investment banks, trading banks, hedge funds, investment management firms, consulting companies, energy and mining companies, regulatory bodies and government organisations

Course intended learning outcomes

1.1 Analyse: access and critically analyse large financial data sets and apply complex financial models and data science techniques to facilitate decision making in financial trading and risk management contexts.
1.2 Synthesise: Demonstrate specialised technical expertise in the field of quantitative finance and data science as expected for a senior professional position in industry, commerce or government.
1.3 Evaluate: Critically analyse, question and evaluate implications of alternative and new models and strategies for financial market trading and risk management.
2.1 Analyse: Critically analyse new financial models to address financial trading and risk management issues.
2.2 Synthesise: Investigate real-world problems by analysing and critically evaluating different solutions to complex challenges.
2.3 Evaluate: Evaluate the application of new research in quantitative finance and data science to complex real world problems.
3.1 Analyse: Demonstrate an awareness of ethical responsibilities of a professional working in the financial sector.
3.2 Synthesise: Develop an awareness of ethical solutions to quantitative finance problems that can result in systemic risk and major impact on society.
3.3 Evaluate: Collaborate to implement mathematical and data science solutions to complex problems arising in the finance and related sectors.
4.1 Analyse: Derive innovative solutions to complex problems in quantitative finance.
4.2 Synthesise: Master the theoretical and practical technical skills in quantitative finance and data science necessary for professional practice.
4.3 Evaluate: Develop the capacity to anticipate and respond to change in quantitative finance.
5.1 Analyse: Convey mathematical, statistical and financial models clearly and fluently, in high quality written form appropriate for their audience.
5.2 Synthesise: Develop and communicate complex solutions to real world problems.
5.3 Evaluate: Prepare and deliver advanced, professional presentations to different audiences to convey problem statements and solutions and place the work in the context of other scholarly research.
6.1 Analyse: Use ethically appropriate and respectful practices when applying mathematical knowledge as related to Aboriginal and Torres Strait Islander communities.
6.2 Synthesise: Acquire cultural awareness for the relevant ethical and respectful practices, when developing community relations.
6.3 Evaluate: Integrate Aboriginal and Torres Strait Islander knowledges and practices when relevant for applying the results of mathematical analysis

Admission requirements

Applicants must have completed a UTS recognised bachelor's degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.

The English proficiency requirement for international students or local applicants with international qualifications is: Academic IELTS: 6.5 overall with a writing score of 6.0; or TOEFL: paper based: 550-583 overall with TWE of 4.5, internet based: 79-93 overall with a writing score of 21; or AE5: Pass; or PTE: 58-64; or CAE: 176-184.

Eligibility for admission does not guarantee offer of a place.

International students

Visa requirement: To obtain a student visa to study in Australia, international students must enrol full time and on campus. Australian student visa regulations also require international students studying on student visas to complete the course within the standard full-time duration. Students can extend their courses only in exceptional circumstances.

Recognition of prior learning

Students may be granted a maximum of 36 credit points of recognition of prior learning.

Course duration and attendance

The course is normally completed in 1.5 years of full-time study or three years of part-time study.

Course structure

The course comprises 96 credit points of core subjects.

Course completion requirements

STM91462 Core Subjects (M Quantitative Finance and Data Science) 96cp
Total 96cp

Course program

Typical full-time programs are provided below, showing a suggested study sequence for students undertaking the course with Autumn and Spring session commencements.

Autumn commencing, full time
Year 1
Autumn session
37005 Fundamentals of Derivative Security Pricing   8cp
37011 Financial Market Instruments   8cp
37010 Statistics and Financial Econometrics   8cp
Spring session
37004 Interest Rates and Credit Risk Models   8cp
37007 Probability Theory and Stochastic Analysis   8cp
37009 Risk Management   8cp
Summer session
37008 Quantitative Portfolio Analysis   8cp
37006 Numerical Methods in Finance   8cp
37003 Computational Methods and Model Implementation   8cp
Year 2
Autumn session
37400 Nonlinear Methods in Quantitative Management   8cp
37457 Advanced Bayesian Methods   8cp
37401 Machine Learning: Mathematical Theory and Applications   8cp
Spring commencing, full time
Year 1
Spring session
37004 Interest Rates and Credit Risk Models   8cp
37007 Probability Theory and Stochastic Analysis   8cp
37009 Risk Management   8cp
Summer session
37008 Quantitative Portfolio Analysis   8cp
37006 Numerical Methods in Finance   8cp
37003 Computational Methods and Model Implementation   8cp
Year 2
Autumn session
37005 Fundamentals of Derivative Security Pricing   8cp
37011 Financial Market Instruments   8cp
37010 Statistics and Financial Econometrics   8cp
Spring session
37400 Nonlinear Methods in Quantitative Management   8cp
37457 Advanced Bayesian Methods   8cp
37401 Machine Learning: Mathematical Theory and Applications   8cp

Articulation with UTS courses

Students who complete C11307 Graduate Certificate in Data Science in Quantitative Finance or C04373 Master of Quantitative Finance can transfer into C04418 Master of Data Science in Quantitative Finance and receive full recognition of prior learning for the subjects already completed.

Other information

Further information is available from:

UTS Student Centre
telephone 1300 ask UTS (1300 275 887)
or +61 2 9514 1222
Ask UTS